Cellular hematopathologies have been traditionally identified and studied by a variety of slide based techniques that include morphological analysis of May-Grunwald/Giemsa or Wright/Giemsa stained blood films and cytoenzymology. Additionally, other techniques, such as cell population analysis by flow cytometry, and molecular methods, such as polymerase chain reaction (PCR) or in situ hybridization to determine gene expression, gene mutations, chromosomal translocations and duplications, have added to the understanding of these pathologies.
Although progress has been made using such techniques in advancing diagnostic capabilities, understanding the mechanisms and the progression of disease, as well as evaluating new therapeutics, such technologies each offer challenges with regard to standardization and robustness, and to a large degree, they have not yet evolved to become routine laboratory tests.
The conventional hematology clinical laboratory includes technologies to rapidly and automatically analyze large numbers of samples of peripheral blood, with minimal human intervention. Companies such as Abbott Laboratories (Abbott Park, Ill.), Beckman Coulter Inc. (Fullerton, Calif.), and TOA Corporation (Kobe, Japan) continue to advance these technologies with regard to throughput levels, the degree of accuracy of the analysis, as well as moderately increasing the information content gathered in each sample run. However, in regard to any sample suggestive of a cellular hematopathology, i.e., falling outside the accepted degree of variance for any particular parameter, traditional slide based methodologies are largely used to determine the probable cause of the abnormality.
Diagnostic criteria in hematology are based on the morphological identification of abnormalities in cell numbers, size, shape and staining patterns. Although these have been supplemented over the past decades with cell population analysis, by staining with monoclonal antibodies to various cell surface determinants and acquiring data via flow cytometry, the most important element in the diagnostic evaluation is the visual inspection of the peripheral blood film, bone marrow and lymph node biopsy using a microscope, which enables a subjective categorization of putative abnormalities.
The manual evaluation of tissue and blood films from patients is tedious, time consuming, and subject to significant intra-laboratory and intra-observer variability. This process suffers from many sources of variability and error, including staining variability (which adversely affects longitudinal analysis), bias of the evaluator, and suboptimal sample preparation (blood films with increased “smudge” cells and atypical lymphocytes). The manual classification of a few hundred cells by morphological appearance results in poor statistical power and low confidence in evaluating observed changes over time, or as a result of treatment.
Chronic lymphocytic leukemia (CLL) is a type of cancer in which the bone marrow produces an excess of lymphocytes (a type of white blood cell) due to a malignant transformation event (e.g., chromosomal translocation). CLL is the most frequent type of leukemia in the Western world. Normally, stem cells (immature cells) develop into mature blood cells by a process of ordered differentiation, which occurs in the bone marrow. There are three types of mature blood cells: (1) red blood cells that carry oxygen to all tissues of the body; (2) white blood cells that fight infection; and, (3) platelets that help prevent bleeding by forming blood clots. Normally, the numbers and types of these blood cells are tightly regulated. In CLL, there is a chronic pathological overproduction of a type of white blood cell called lymphocytes. There are three types of lymphocytes: (1) B lymphocytes that make antibodies to help fight infection; (2) T lymphocytes that help B lymphocytes make antibodies to fight infection; and, (3) killer cells that attack cancer cells and viruses. CLL is a disease involving an increase in B lymphocyte cell numbers in the peripheral blood, usually reflective of a clonal expansion of a malignantly transformed CD5+B lymphocyte cell.
Currently, established chemotherapeutic treatments are used to treat this condition, but a number of newer therapeutics, involving monoclonal antibodies to cell surface antigens expressed on CLL cells (e.g., Rituximab), have been developed. Recent data from the National Cancer Data Base indicate that the 5-year survival for this disease condition is about 48%, with only 23% of patients surviving the disease condition after 10 years. Recently, a number of prognostic factors have been identified that allow stratification of the patient population into two subpopulations with distinct clinical outcomes. Factors that tend to correlate with decreased survival are: ZAP7O expression (a tyrosine kinase required for T lymphocyte cell signaling), increased CD38 expression, un-mutated Ig Vh genes, and chromosomal abnormalities. However, routine assessment of these factors has not evolved to a standard clinical practice, due to technical challenges with data standardization and interpretation.
Morphological evaluation remains the “gold standard” in the assessment of hematopathologies, and patients with CLL present with morphological heterogeneity. Attempts to correlate a particular morphological profile with clinical prognosis have been made, but to date, no association has been widely accepted, and the morphologic sub-classification of CLL and its correlation with clinical prognosis remains to be explored.
It would therefore be desirable to provide a method and apparatus suitable for automatically analyzing blood, including peripheral blood leukocytes, and cellular components such as bone marrow and lymph nodes (whose cells are readily amenable to being processed in suspension), to facilitate researching blood related diseases and abnormalities. It would be particularly desirable to provide a method and apparatus for rapidly collecting imagery from blood and other bodily fluids (and cellular compartments), and to provide software tools for analyzing such imagery to identify cellular abnormalities or cellular distribution abnormalities associated with a disease condition.